events_loader.py

import json
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
import os

OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
def load_events(filename):
    with open(filename, 'r') as f:
        events_data = json.load(f)
    return events_data

def store_events_in_faiss(events_data):
    embeddings = []
    event_summaries = []

    for event in events_data:
        summary = summarize_event(event)  # Function to extract relevant summary text
        event_summaries.append(summary)
        embeddings.append(embed_text(summary))  # Function to embed summary using OpenAI

    # Initialize FAISS and store embeddings
    openai_embedding_model = OpenAIEmbeddings(model="text-embedding-ada-002", openai_api_key=OPENAI_API_KEY)
    faiss_db = FAISS.from_texts(texts=event_summaries, embedding=openai_embedding_model)
    return faiss_db

def summarize_event(event):
    # Initialize summary components
    event_type = event.get('event_type', 'Unknown')
    object_ = event.get('object', {}).get('label', 'Unknown')
    location = event.get('location', 'Unknown')
    timestamp = event.get('timestamp', 'Unknown')
    
    # Create summary string
    summary = f"Event Type: {event_type}, Object: {object_}, Location: {location}, Timestamp: {timestamp}"
    return summary

def embed_text(text):
    # Placeholder for embedding logic using OpenAI
    return text

filename = 'camera.json'
events_data = load_events(filename)
faiss_db = store_events_in_faiss(events_data)